This invention relates to imaging processing systems, and in particular methods and systems for extremely efficient estimation of spectral covariance matrices conveying information related to the background of a hyperspectral scene, including covariance matrices that have had outliers removed (decontaminated) for processing of spectral, especially hyperspectral imaging (HSI) data. The term “hyperspectral” refers to imaging narrow spectral bands over a continuous spectral range, and producing the spectra of all scene pixels in a hyperspectral scene.
In many conventional HSI systems, hyperspectral sensors collect data of an image from one spatial line and disperse the spectrum across a perpendicular direction of the focal plane of the optics receiving the image. Thus a focal plane pixel measures the intensity of a given spot on the ground in a specific waveband. A complete HSI cube scene is formed by scanning this spatial line across the scene that is imaged. The complete HSI cube may be analyzed as a measurement of the spectrum, the intensity in many wavebands, for a spatial pixel. This spatial pixel represents a given spot on the ground in a cross-scan direction for one of the lines at a given time in the scan direction. These data may be analyzed to separate and evaluate differing wavelengths, which may permit finer resolution and greater perception of information contained in the image. From such data, hyperspectral imaging systems may be able to detect targets or spectral anomalies, including materials, and changes to an image.
For any anomaly or target detection algorithm to be successful, the anomaly or target spectrum should be distinguishable from background spectra. Detection filters assume that targets are statistically rare in the scene and do not occupy very many pixels. The filters work against a wide distribution of backgrounds and are optimized for backgrounds that have a Gaussian distribution. A spectral covariance matrix COV may also be computed to determine the eigenvectors of the scene. To obtain a faithful representation of all eigenvectors, the covariance matrix must accurately represent the scene, which has often led others to avoid sampling pixels. Those that have tried sampling fewer pixels to construct the COV have found wildly varying covariance estimates.
Previous approaches to computing COV's have required 0.5*P*D2 operations to compute, where P is the number of pixels (e.g., 106-107) or spatial locations in the scene, and D is the number of wavebands measured by the HSI sensor (e.g., 100-480). Typically, the scene background clutter is described by a mean vector and a covariance matrix. Possible presence of anomalies and/or targets in the background estimation data may skew the values of the spectral covariance matrix. This may lead to significant performance degradation; therefore, it is important that estimations be performed using sets of “target-free” pixels. Some prior approaches attempt to remove pixels that skew the COV, for example, by setting aside pixels with the top fraction (e.g., 1-2%) of RX anomaly scores. Removing outliers has been shown to significantly increase both anomaly detection scores and target detection scores, but is computationally expensive. Prior approaches use all non-contaminating scene pixels and all hyperspectral dimensions in the computation of detection scores for targets and anomalies. The computation of anomaly scores to identify contaminants requires an addition P*D2 operations, and recalculation of the covariance matrix without these contaminants requires 0.5*P*D2 operations, resulting in the required number of computational operations to be on the order of 2P*D2 operations,
Thus, what is needed is a highly computationally efficient and accurate estimation of the background spectral content of a HSI scene, represented by a spectral covariance matrix, especially one that has had outliers pixels removed (“decontaminated”).